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A high efficiency approximation of EnKF for coupled model data assimilation

Banff International Research Station for Mathematical Innovation and Discovery

To implement Bayes’ Theorem for data assimilation, an ensemble Kalman filter (EnKF) uses a set of model integrations to simulate the temporally-varying background probability distribution function. Due to the merit of derived data assimilation solution coherently combining model and observational information, EnKF has risen as a widely-promising data assimilation algorithm in weather and climate studies. However, its huge computational resource demanding for ensemble model integrations sets a significant limitation on applications in high resolution coupled earth systems. Given that the background error statistics consist of stationary, slow-varying and fast varying parts, a high efficiency approximate EnKF (Hea-EnKF) is designed to dramatically enhance the computational efficiency. The Hea-EnKF is a combination of stationary, slow-varying and fast-varying filters, implemented in regressions sampled from large size single model solution data and updated with the model integrations. Validation shows that due to improved representation on stationary and slow-varying background statistics, the Hea-EnKF while only requiring a small fraction of computer resources can be better than the standard EnKF that uses finite ensemble statistics. The new algorithm makes practical to assimilate multi-source observations into any high-resolution coupled model intractable with current super-computing power for weather-climate analysis and predictions.

Mathematics; Partial differential equations; Dynamical systems and ergodic theory

video/mp4

Author affiliation: Ocean University of China

2018-05-21

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/66014

Unreviewed

http://creativecommons.org/licenses/by-nc-nd/4.0/